Identifying similar facial features is interesting to individuals and arises in multiple contexts. One example context is with a child's facial features. Individuals identify which facial feature is similar to that of which parent or relative. For example, an individual may conclude “He has his mother's eyes and his father's nose.” Another example context is with celebrities' facial features. Individuals are curious about which celebrity their facial feature is most similar to. For example, the individual desires to know which celebrity has the most similar nose, or whether the individual's nose is more similar to that of celebrity A or celebrity B. Yet another context is with doppelgangers. An individual desires to know whether anyone else has a near identical facial feature. For example, the individual has a distinct chin and wants to identify if anyone else has a similar chin.
A problem is that current systems do not identify similar facial features in most cases. Specifically, current systems do not identify at least one facial feature from a plurality of facial images as most similar to the facial feature of a query facial image. More specifically, the current systems do not search facial images retrieved from an image search, an individual's social network or electronic database to identify an image with a facial feature most similar to the query image.